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Add PostgreSQL-backed memory, editable evidence with source review and activation, and human-approved archive repairs across the harness, API, and UI. Include migrations, deployment support, regression coverage, and validation documentation. Refresh permissions from validated session roles so existing administrator logins can access newly deployed archive management features.
64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
"""Installation binding for Memory; vector dependencies are opened only when needed."""
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import os
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from tht.session.models import PrincipalContext
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from .migrate import installation_url
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from .models import MemoryForbidden, MemoryUnavailable
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from .repository import MemoryRepository
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from .service import MemoryService
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def _principal():
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issuer = os.environ.get("THT_PRINCIPAL_ISSUER", "").strip()
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subject = os.environ.get("THT_PRINCIPAL_SUBJECT", "").strip()
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if not issuer or not subject:
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raise MemoryForbidden("A trusted runtime principal is required for Memory")
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return PrincipalContext(
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issuer=issuer, subject=subject,
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is_admin=os.environ.get("THT_PRINCIPAL_IS_ADMIN", "").lower() in {"1", "true"},
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)
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def _repository(workspace_id):
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try:
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url = installation_url()
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except ValueError:
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raise MemoryUnavailable("Memory PostgreSQL installation configuration is unavailable") \
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from None
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return MemoryRepository(url, workspace_id)
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def memory_service(cfg):
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from tht.adapters.factory import build_vector_store
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from tht.cli.vector_cmd import make_embedder
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principal = _principal()
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return MemoryService(_repository(cfg._workspace_id), principal,
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language=cfg.language,
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store_factory=lambda: build_vector_store(cfg, require_write=True),
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embedder_factory=lambda: make_embedder(cfg.embeddings))
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def admin_service(workspace_id, runtime):
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"""Admin access needs no DWH binding, active session or Evidence materialization."""
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from tht.adapters.vector.qdrant import QdrantVectorStore
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from tht.config import EmbeddingsConfig
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from tht.vectorstore.embeddings import OllamaEmbeddings
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principal = _principal()
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if not principal.is_admin:
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raise MemoryForbidden("Memory administration requires an administrator")
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return MemoryService(_repository(workspace_id), principal,
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language=runtime.get("memoryLanguage", "en"),
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store_factory=lambda: QdrantVectorStore(
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base_url=runtime["internalQdrantUrl"], workspace_id=workspace_id,
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collections={"reference": workspace_id+"-reference", "memory": workspace_id+"-memory"},
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expected_dimension=runtime["internalEmbeddingDimensions"],
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),
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embedder_factory=lambda: OllamaEmbeddings(EmbeddingsConfig(
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base_url=runtime["internalEmbeddingUrl"], model=runtime["internalEmbeddingModel"],
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dimensions=runtime["internalEmbeddingDimensions"], timeout=30,
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)))
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